Gapminder World Data which collected a lot of information about how people live their lives in different countries, tracked across the years, and on a number of different indicators.
I select 9 key metrics to understand the state of the world and the global development:
(1) Hunger and Undernourishment
(2) Child Mortality
(3) Education
(4) GDP per capita
(5) Extreme Poverty
(6) Population
(7) Fertility Rate
(8) Energy Use
(9) CO2 emissions
Below are the description of each dataset gathered from Gapminder
(1)
sn_itk_defc_zs.csv: Prevalence of undernourishment (% of population).
(2)
child_mortality_0_5_year_olds_dying_per_1000_born.csv: Death of children under five years of age per 1000 live births
(3)
literacy_rate_adult_total_percent_of_people_ages_15_and_above.csv: Adult literacy rate is the percentage of people ages 15 and above who can, with understanding, read and write a short, simple statement on their every day life.
(4)
income_per_person_gdppercapita_ppp_inflation_adjusted.csv: Gross Domestic Product per person adjusted for differences in purchasing power (in international dollars, fixed 2017 prices, PPP based on 2017 ICP).
(5)
poverty_percent_people_below_320_a_day.csv: poverty headcount ratio at 3.20 dollar a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.
(6)
population_total.csv: Total Population.
(7)
sp_dyn_tfrt_in.csv: Fertility rate, total (births per woman).
(8)
energy_use_per_person.csv: Refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport (Kg Of Oil Equivalent Per Person).
(9)
co2_emissions_tonnes_per_person.csv: Carbon dioxide emissions from burning of fossil fuels (metric tonnes of CO2 per person).
# All the packages that I will use in the investigation
import pandas as pd
import numpy as np
import pycountry # Gets countries code
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
%matplotlib inline